Ranking models lie at the heart of research on information retrieval (IR). During the past
decades, different techniques have been proposed for constructing ranking models, from …

We propose a Joint Neural Collaborative Filtering (J-NCF) method for recommender
systems. The J-NCF model applies a joint neural network that couples deep feature learning …

As a popular Q&A site for programming, Stack Overflow is a treasure for developers.
However, the amount of questions and answers on Stack Overflow make it difficult for …

We present a context-aware neural ranking model to exploit users' on-task search activities
and enhance retrieval performance. In particular, a two-level hierarchical recurrent neural …

Large Language Models (LLMs) excel at tackling various natural language tasks. However,
due to the significant costs involved in re-training or fine-tuning them, they remain largely …

Context information in search sessions has proven to be useful for capturing user search
intent. Existing studies explored user behavior sequences in sessions in different ways to …

It has been shown that the interpretability of search results is enhanced when query aspects
covered by documents are explicitly provided. However, existing work on aspect-oriented …

Session-based recommendation is the task of recommending the next item a user might be
interested in given partially known session information, eg, part of a session or recent …

When conducting a search task, users may find it difficult to articulate their need, even more
so when the task is complex. To help them complete their search, search engine usually …

Users' complex information needs usually require consecutive queries, which results in
sessions with a series of interactions. Exploiting such contextual interactions has been …